Exploring compression and parallelization techniques for distribution of deep neural networks over Edge–Fog continuum – a review

Abstract

<jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title><jats:p>DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Originality/value</jats:title><jats:p>To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.</jats:p></jats:sec>

Probabilistic Hesitant Fuzzy Methods for Prioritizing Distributed Stream Processing Frameworks for IoT Applications

“…It has been widely used in various domains, such as transportation, health care, logistics, and agriculture [2]. In the IoT applications, millions of IoT devices are deployed and they continuously output large amounts of data [3], which are valuable for the enterprises to make reasonable business decisions in realtime [4]. However, how to process and analyze the IoT stream data are a big challenge for enterprises since traditional batch processing architecture cannot process large amounts of data in realtime.…”

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

“…Deep learning has been applied in several areas, including detecting fake news, self-driving cars, healthcare, visual recognition, and entertainment (Ghosh et al, 2021 ; Kar et al, 2019 ; Smith & Lovgren, 2018 ). Deep learning technology has also been successfully used in predictive planning, manufacturing, supply chain management, scheduling, forecasting, capacity allocation, inventory optimization, and so on (Chatterjee et al, 2021 ; Murphy & de Jongh, 2011 ; Nazir et al, 2020 ).…”

“…Deep learning can be conceptualized as investigating existing cognitive structures and establishing various links to other concepts, realities, and ideas (Biggs, 1999 ; Entwistle, 1989 ). Deep learning models are concerned with areas such as time-setting data management, financial issues (Chatterjee et al, 2020a ; Harmancioglu et al, 2010 ; Kumar et al, 2018 ; Nazir et al, 2020 ). In this context, predictive maintenance capability seems to play an important role in influencing firms to adopt smart manufacturing systems (Hassan, 2017 ).…”

Low Latency Deep Learning Inference Model for Distributed Intelligent IoT Edge Clusters

“…create an ecosystem for achieving inference models with excellent performance. In this regard, many attempts have been reported to optimize the DNN models at edge devices [61], [62]. While Communication load, communication overhead, cost, memory, processing speed, network bandwidth, jitter, complexity are a few performance parameters, much of the preliminary research has focused on low-latency and energyefficient computations.…”